Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives
Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …
classifying whether each pixel of the image is water or not, has become a hot issue in the …
U-Net-STN: A novel end-to-end lake boundary prediction model
Detecting changes in land cover is a critical task in remote sensing image interpretation, with
particular significance placed on accurately determining the boundaries of lakes. Lake …
particular significance placed on accurately determining the boundaries of lakes. Lake …
Deep learning-based semantic segmentation of remote sensing images: a review
Semantic segmentation is a fundamental but challenging problem of pixel-level remote
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …
CIMFNet: Cross-layer interaction and multiscale fusion network for semantic segmentation of high-resolution remote sensing images
Semantic segmentation of remote sensing images has received increasing attention in
recent years; however, using a single imaging modality limits the segmentation …
recent years; however, using a single imaging modality limits the segmentation …
Multi-scale feature aggregation network for water area segmentation
K Hu, M Li, M **a, H Lin - Remote Sensing, 2022 - mdpi.com
Water area segmentation is an important branch of remote sensing image segmentation, but
in reality, most water area images have complex and diverse backgrounds. Traditional …
in reality, most water area images have complex and diverse backgrounds. Traditional …
Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images
Since DCNNs (deep convolutional neural networks) have been successfully applied to
various academic and industrial fields, semantic segmentation methods, based on DCNNs …
various academic and industrial fields, semantic segmentation methods, based on DCNNs …
NT-Net: A semantic segmentation network for extracting lake water bodies from optical remote sensing images based on transformer
HF Zhong, Q Sun, HM Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The automatic extraction of lake water is one of the research hotspots in the field of remote
sensing image processing. Due to the small interclass variance between lakes and other …
sensing image processing. Due to the small interclass variance between lakes and other …
MU-Net: Embedding MixFormer into Unet to Extract Water Bodies from Remote Sensing Images
Y Zhang, H Lu, G Ma, H Zhao, D **e, S Geng, W Tian… - Remote Sensing, 2023 - mdpi.com
Water bodies extraction is important in water resource utilization and flood prevention and
mitigation. Remote sensing images contain rich information, but due to the complex spatial …
mitigation. Remote sensing images contain rich information, but due to the complex spatial …
[HTML][HTML] Panoptic segmentation meets remote sensing
Panoptic segmentation combines instance and semantic predictions, allowing the detection
of countable objects and different backgrounds simultaneously. Effectively approaching …
of countable objects and different backgrounds simultaneously. Effectively approaching …
Intelligent image semantic segmentation: a review through deep learning techniques for remote sensing image analysis
Image semantic segmentation is an important part of fundamental in image interpretation
and computer vision. With the development of convolutional neural network technology …
and computer vision. With the development of convolutional neural network technology …